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As a Senior Full Stack Software Engineer, you will embed with enterprise customers to implement AI systems, design workflows, and operationalize software solutions, enhancing efficiency and driving measurable outcomes.

Own support analytics for User Operations: build recurring reports and self-serve dashboards, track north-star metrics (cSAT, TTR, QA, escalations), instrument Zendesk, connect support data to product/customer sources, quantify operational cost of bugs, measure AI-driven support impact, and deliver ad hoc analyses to inform Product, Engineering, and leadership.

Design and build scalable dbt pipelines and semantic models to turn product event data into reliable analytics. Define tracking, governance, and metric standards; partner with Product, GTM, Finance, and execs to deliver self-serve dashboards and data products that drive decision-making.

Lead go-to-market strategy for a core product area: define positioning, plan and execute launches, drive post-launch adoption, create sales and customer assets, conduct customer and competitive research, and partner cross-functionally with Product, Sales, Growth, and Enablement to drive adoption and revenue.

The role involves developing AI systems for legal research, collaborating with engineering and product teams, and understanding client workflows to deliver solutions.

This role involves designing and building end-to-end production systems across frontend, backend, and data systems for new legal product capabilities at Harvey.

Own research and implementation of post-training methods for legal language models, including supervised fine-tuning, reinforcement learning, reward design, verifiers, preference modeling, evaluations, and distributed training. Lead experiments from research direction through production deployment, while debugging training instability, reward hacking, and divergence. Build rigorous evaluation systems and production-quality Python code for long-horizon legal reasoning and agentic workflows.

Build and scale infrastructure for post-training open-weight language models, including distributed training, reinforcement learning rollouts, agent environments, evaluation systems, experiment tracking, and model serving. Own performance, reliability, GPU utilization, checkpointing, failure recovery, observability, and production integration. Partner closely with researchers to turn experimental workflows into dependable systems and accelerate development of legal AI capabilities.

Build synthetic data pipelines, evaluation systems, benchmarks, verifiers, rubrics, and quality metrics for legal AI models and agentic workflows. Partner with researchers, legal experts, and customers to investigate model failures, validate metrics, maintain data quality and provenance, and convert expert feedback and product usage into training signals and improved evaluations.

Own Harvey’s shared cloud-first IT infrastructure, infrastructure-as-code frameworks, automation, provisioning, monitoring, backups, resilience, certificates, DNS, and office technology standards. Build CI pipelines and configuration management for platforms such as Okta, Jamf, Intune, and Google Workspace. Improve change control, documentation, alerting, recovery runbooks, and identity lifecycle workflows while partnering with Security, Networking, and platform owners.